1. Field of the Invention
The present application generally relates to business monitoring systems and, more particularly, to a model driven approach to enhance existing business performance models with predictive modeling capabilities.
2. Background Description
For an enterprise to be competitive, the ability to perform predictive analysis on large amount of data is very important to analyze a trend, discover the paint points, and/or discover new opportunities. Most companies today implement various Business Performance Management solutions including Business Intelligence techniques that help determine the current state of the business. This is achieved by defining metrics or key performance indicators organized in a hierarchy through the various vertical and horizontal silos of the organization. Data and events received in real time are persisted in a data mart and are used to provide historical analysis summarizing what has happened in the past. In other words, historical analysis can reveal who the best customers were last month and who they were this month. This kind of traditional analysis cannot reveal what will happen in the future. Predictive analysis discovers meaningful patterns and relationships in data separating the signals from the noise thereby helping in improved decision making. Business process monitoring models currently lack the ability to incorporate the meta-data for such predictive models, which restricts the models' ability to capture such metrics. There are currently limited modeling capabilities and supporting tools to capture the metric definition, relationships, dimensions, semantics and their management. The available tools today limit metrics modeled as hierarchical structure with value dependency. This implies that the existing models are also not sophisticated enough to capture the relationship of time as a dimension to allow for look-ahead prediction of the metrics such as dynamic systems models, time-series based models, forecasting, propensity and scoring models. Combining predictive analysis with organization business process and performance management provides insight into critical business issues and enables proactive decision making and risk management amongst other benefits.
There are many subsystems available that provide prediction capabilities. Among these are general purpose systems such as data mining tools and system dynamics. Data mining tools provide scoring models and predictions based on historical data; however, data mining tools do not provide metric values but can determine qualitative relationships. It is objective in nature. System dynamics use continuous modeling to predict values of metrics based on the specified time dimension; however, system dynamics is subjective since it is based on the user's perception of the metric network and relationships.
The integration of these predictive systems with standard business process monitoring and management systems has always been a challenge. Business monitoring systems are built using metrics catering to the current and in some cases historical aspects of the business whereas predictive models look ahead in time. The current invention features a novel mix of both these capabilities in order to provide a system and method for predictive metric analysis to a business monitoring subsystem